Determining phase-encoding direction for parallel MRI

ABSTRACT

Example systems, methods, and apparatus associated with determining a phase-encoding direction for parallel MRI are described. One example, method includes selecting a set of projection directions along which an MRI apparatus is to apply RF energy to an object to be imaged. The method includes controlling the MRI apparatus to selecting a set of projection directions and to acquire MR signal from the object through a set of detectors. The method includes analyzing the MR signal to identify individual sensitivities for members of the set of detectors and selecting a phase-encoding direction for a pMRI session based on the individual sensitivities for the members. The method produces a concrete, tangible, and useful result by controlling the MRI apparatus to perform the pMRI session based on the selected phase-encoding direction.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Patent60/927,178, filed May 2, 2007, by the same inventors.

COPYRIGHT NOTICE

A portion of the disclosure of this patent document contains materialthat is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction of the patent document or thepatent disclosure as it appears in the Patent and Trademark Officepatent file or records, but otherwise reserves all copyright rightswhatsoever.

BACKGROUND

A Magnetic Resonance Imaging (MRI) apparatus that performs parallel MRI(pMRI) may include an array of detectors. The array of detectors may bereferred to as an array of coils. The pMRI apparatus may acquire signalsfrom the array of detectors. The detectors may be arranged in a phasedarray of coils where individual coils in the phased array may havelocalized sensitivity. While the sensitivities of different coils areconceptually smooth over a field of view (FOV), actual imageacquisitions may yield different results than that predicted by atheoretical design. Thus, reconstruction of a magnetic resonance (MR)image from signals acquired from a phased array of coils associated witha pMRI apparatus may depend on understanding the actual sensitivity andspatial encoding capabilities of a coil(s) during a pMRI session.

Spatial encoding capabilities of the members of the set of detectors mayvary with respect to, for example, orientation to a projectiondirection. For example, a set of coils oriented perpendicular to aprojection direction may produce a first spatial encoding capability. Aset of coils oriented parallel to a projection direction may produce asecond spatial encoding capability. Additionally, when coils areoriented at varying angles with respect to a projection directionvarying spatial encoding capabilities may be available. Thus, pMRIperformance may depend on the choice of a phase-encoding directionemployed in an MRI session due to the effect on spatial encodingcapabilities of members of a phased array of coils. This pMRIperformance may be reflected, for example, in image reconstructionquality. Conventionally, selecting a phase-encoding direction in pMRImay not have occurred or may have been uninformed and thus may haveproduced sub-optimal results.

BRIEF DESCRIPTION OF DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of the specification, illustrate various example systems, methods,and other example embodiments of various aspects of the invention. Itwill be appreciated that the illustrated element boundaries (e.g.,boxes, groups of boxes, or other shapes) in the figures represent oneexample of the boundaries. One of ordinary skill in the art willappreciate that in some examples one element may be designed as multipleelements or that multiple elements may be designed as one element. Insome examples, an element shown as an internal component of anotherelement may be implemented as an external component and vice versa.Furthermore, elements may not be drawn to scale.

FIG. 1 illustrates an example method for selecting a phase-encodingdirection.

FIG. 2 illustrates a set of different projection directions relative toan array of detectors in an MRI apparatus.

FIG. 3 illustrates a first projection direction that will yield asubstantially constant signal in members of array 300.

FIG. 4 illustrates a second projection direction that will yield varyingsignals in members of array 400.

FIG. 5 illustrates an example method for selecting a phase-encodingdirection.

FIG. 6 illustrates an example method for selecting a phase-encodingdirection.

FIG. 7 illustrates an apparatus for controlling phase-encoding directionin pMRI.

FIG. 8 illustrates an MRI apparatus configured with a phase-encodingdirection control logic.

FIG. 9 illustrates a computing device in which example methods describedherein may be performed to control an MRI apparatus with respect tophase-encoding direction.

DETAILED DESCRIPTION

Example systems, methods, and apparatus facilitate selectingphase-encoding directions for a pMRI session. In one example, aphase-encoding direction may be selected that optimizes spatial encodingand thus signal acquisition for a pMRI session. In another example, aset of phase-encoding directions may be tested and one of the testeddirections may be selected. While this may not yield optimal spatialencoding, it may yield better spatial encoding than an uninformed orrandom selection. In another example, a set of phase-encoding directionsmay be sampled and then a “zeroing-in” process may be undertaken toquickly arrive at a desired phase-encoding direction. Selecting aphase-encoding direction based on real-time feedback from an actual pMRIsession facilitates improving reconstruction quality of pMRI byfacilitating improved spatial encoding.

FIG. 2 illustrates different projection directions that may be tested. Afirst projection may be taken along axis 210. Subsequent projections maybe taken along axis 220, axis 230, and/or axis 240. While fourdirections are illustrated, it is to be appreciated that a greaterand/or lesser number of directions may be used. These directions havedifferent relationships (e.g., angles) to the phased array coils 200. Asdescribed above, the phase-encoding direction selected affects the coilsensitivity profile. Example systems, methods, and apparatus facilitateselecting a desired phase-encoding direction before a complete MRIsession is undertaken by making at least two projections having at leasttwo different directions.

Example systems, methods, and apparatus perform pre-session and/or insession feedback analysis to determine a desired phase-encodingdirection. In one example, the desired phase-encoding direction may bean optimal phase-encoding direction. In another example, the desiredphase-encoding direction may be the direction that yields the highestspatial encoding from directions tested. For example, rather thantesting all possible phase-encoding directions, a small set ofdirections may be tested and an optimal direction may be selected. Inanother example, different phase-encoding directions may be tested untila threshold spatial-encoding property is achieved.

Feedback analysis includes acquiring data along a set of test projectiondirections, analyzing that data with respect to the spatial encodingcapabilities of the detectors in relation to the test projectiondirections, and selecting a phase-encoding direction based on theanalysis. While a pre-session analysis that includes acquiring data isdescribed, it is to be appreciated that in one example previouslyacquired data may be decomposed into multiple projections using, forexample, a Radon transform. The previously acquired data may have beenacquired from a localizer image, a scout image, and so on.

Example systems, methods, and apparatus may decompose acquired signalsinto orthogonal basis sets. Acquired signals may be decomposed using,for example, principal component analysis (PCA), singular valuedecomposition (SVD), quadratic residue decomposition (QR), spectraldecomposition, polar decomposition, and so on. The decompositionfacilitates determining a phase-encoding direction. The decompositionmay be referred to herein as a “signal decomposition”. PCA is one lineartransformation that can decompose a data set in terms of orthogonalbasis functions. Thus, different data sets acquired by the detectorsassociated with projections along different directions can be decomposedin terms of orthogonal basis functions using PCA or other signaldecompositions.

Basis functions, also known as principal components, may be generated bycomputing eigenvalues and eigenvectors of a covariance matrix of a setof data that is acquired. Different sets of data may be associated withprojections along different directions. Performing signal decompositionusing, for example, PCA, SVD, QR, and so on, for a data set acquiredfrom pMRI apparatus detectors (e.g., phased array coils) may cause acoordinate system shift for the data set. In the new coordinate system,principle components (e.g., eigenmodes) of the detectors may be thebasis functions.

Detectors in a parallel MRI apparatus may be arranged in a phased arrayof coils. Individual coils may receive signals associated with aprojection along a spatial direction. Individual coils may havedifferent sensitivities based on their directional relationship to aprojection and their location in a phased array. Thus, different coilsmay make different contributions to a received signal. Performing signaldecomposition on an acquired signal facilitates computing eigenvaluesthat describe contributions of individual principle components.Therefore, data (e.g., eigenvalues) is available to measure an encodingcapability of a phased array. Higher contributions of high-orderprinciple components indicate a higher (e.g., better) encodingcapability of a phased array. The higher encoding capability resultsfrom having more independent basis functions available for encoding andthus having more data available for analysis.

In one example, analysis may employ the following equation, which isderived from the SMASH (simultaneous acquisition of spatial harmonics)method for parallel imaging:

$\begin{matrix}{{\sum\limits_{e}{n_{e}^{1}{C_{e}(x)}}} = ^{{{}_{}^{\; \Delta}{}_{}^{}}X}} & {{Equation}\mspace{14mu}\lbrack 1\rbrack}\end{matrix}$

where:

n represents a linear weight, and

C represents a coil sensitivity profile.

Equation [1] describes the combination of multiple coil sensitivityprofiles to form a complex spatial harmonic sensitivity profile. Theeffect approximates applying a phase-encoding gradient that modulates aspin-density with a spatial harmonic.

In vivo coil sensitivity calibrations facilitate accurate pMRIreconstruction. Coil sensitivity calibrations may themselves depend oncharacteristics (e.g., projection direction) of a pre-scan analysisand/or data derived from a decomposition of previously acquired data.FIGS. 3 and 4 illustrate how projection direction may affect coilsensitivity. FIG. 3 illustrates a pre-scan projection that leads to avalue (e.g., C₁(x)) used in determining the coil sensitivity of a coilbeing nearly a constant. FIG. 4 illustrates a pre-scan projection thatleads to the value not being a constant. A signal acquired using theprojection direction 300 would provide less information for analysis andprocessing than a signal acquired using the projection direction 400.Since C₁(x) is nearly a constant for the projection 300 illustrated inFIG. 3, analysis results would be inferior to analysis that occurs whenC₁(x) is not a constant, as would result from the projection 400illustrated in FIG. 4. Conventionally, whether an object and aprojection would produce the situation illustrated in FIG. 3 or thesituation illustrated in FIG. 4 may not have been known. Therefore,example systems, methods, and apparatus acquire signal in response todifferent projection directions and then control a pMRI session based,at least in part, on analysis of the different signal acquired.Performing pre-scan projections along different directions facilitatesgathering data that facilitates identifying different situations whichin turn facilitates improving coil sensitivity calibrations which inturn may improve pMRI performance.

The following includes definitions of selected terms employed herein.The definitions include various examples and/or forms of components thatfall within the scope of a term and that may be used for implementation.The examples are not intended to be limiting. Both singular and pluralforms of terms may be within the definitions.

References to “one embodiment”, “an embodiment”, “one example”, “anexample”, and so on, indicate that the embodiment(s) or example(s) sodescribed may include a particular feature, structure, characteristic,property, element, or limitation, but that not every embodiment orexample necessarily includes that particular feature, structure,characteristic, property, element or limitation. Furthermore, repeateduse of the phrase “in one embodiment” does not necessarily refer to thesame embodiment, though it may.

ASIC: application specific integrated circuit.

CD: compact disk.

CD-R: CD recordable.

CD-RW: CD rewriteable.

DVD: digital versatile disk and/or digital video disk.

HTTP: hypertext transfer protocol.

LAN: local area network.

PCI: peripheral component interconnect.

PCIE: PCI express.

RAM: random access memory.

DRAM: dynamic RAM.

SRAM: synchronous RAM.

ROM: read only memory.

PROM: programmable ROM.

USB: universal serial bus.

WAN: wide area network.

“Computer component”, as used herein, refers to a computer-relatedentity (e.g., hardware, firmware, software in execution, combinationsthereof. Computer components may include, for example, a process runningon a processor, a processor, an object, an executable, a thread ofexecution, and a computer. A computer component(s) may reside within aprocess and/or thread. A computer component may be localized on onecomputer and/or may be distributed between multiple computers.

“Computer communication”, as used herein, refers to a communicationbetween computing devices (e.g., computer, personal digital assistant,cellular telephone) and can be, for example, a network transfer, a filetransfer, an applet transfer, an email, an HTTP transfer, and so on. Acomputer communication can occur across, for example, a wireless system(e.g., IEEE 802.11), an Ethernet system (e.g., IEEE 802.3), a token ringsystem (e.g., IEEE 802.5), a LAN, a WAN, a point-to-point system, acircuit switching system, a packet switching system, and so on.

“Computer-readable medium”, as used herein, refers to a medium thatstores signals, instructions and/or data. A computer-readable medium maytake forms, including, but not limited to, non-volatile media, andvolatile media. Non-volatile media may include, for example, opticaldisks, magnetic disks, and so on. Volatile media may include, forexample, semiconductor memories, dynamic memory, and so on. Common formsof a computer-readable medium may include, but are not limited to, afloppy disk, a flexible disk, a hard disk, a magnetic tape, othermagnetic medium, an ASIC, a CD, other optical medium, a RAM, a ROM, amemory chip or card, a memory stick, and other media from which acomputer, a processor or other electronic device can read.

“Data store”, as used herein, refers to a physical and/or logical entitythat can store data. A data store may be, for example, a database, atable, a file, a list, a queue, a heap, a memory, a register, and so on.In different examples, a data store may reside in one logical and/orphysical entity and/or may be distributed between two or more logicaland/or physical entities.

“Logic”, as used herein, includes but is not limited to hardware,firmware, software in execution on a machine, and/or combinations ofeach to perform a function(s) or an action(s), and/or to cause afunction or action from another logic, method, and/or system. Logic mayinclude a software controlled microprocessor, a discrete logic (e.g.,ASIC), an analog circuit, a digital circuit, a programmed logic device,a memory device containing instructions, and so on. Logic may includeone or more gates, combinations of gates, or other circuit components.Where multiple logical logics are described, it may be possible toincorporate the multiple logical logics into one physical logic.Similarly, where a single logical logic is described, it may be possibleto distribute that single logical logic between multiple physicallogics.

An “operable connection”, or a connection by which entities are“operably connected”, is one in which signals, physical communications,and/or logical communications may be sent and/or received. An operableconnection may include a physical interface, an electrical interface,and/or a data interface. An operable connection may include differingcombinations of interfaces and/or connections sufficient to allowoperable control. For example, two entities can be operably connected tocommunicate signals to each other directly or through one or moreintermediate entities (e.g., processor, operating system, logic,software). Logical and/or physical communication channels can be used tocreate an operable connection.

“Signal”, as used herein, includes but is not limited to, electricalsignals, optical signals, analog signals, digital signals, data,computer instructions, processor instructions, messages, a bit, a bitstream, or other means that can be received, transmitted and/ordetected.

“Software”, as used herein, includes but is not limited to, one or moreexecutable instruction that cause a computer, processor, or otherelectronic device to perform functions, actions and/or behave in adesired manner. “Software” does not refer to stored instructions beingclaimed as stored instructions per se (e.g., a program listing). Theinstructions may be embodied in various forms including routines,algorithms, modules, methods, threads, and/or programs includingseparate applications or code from dynamically linked libraries.

“User”, as used herein, includes but is not limited to one or morepersons, software, computers or other devices, or combinations of these.

Some portions of the detailed descriptions that follow are presented interms of algorithms and symbolic representations of operations on databits within a memory. These algorithmic descriptions and representationsare used by those skilled in the art to convey the substance of theirwork to others. An algorithm, here and generally, is conceived to be asequence of operations that produce a result. The operations may includephysical manipulations of physical quantities. Usually, though notnecessarily, the physical quantities take the form of electrical ormagnetic signals capable of being stored, transferred, combined,compared, and otherwise manipulated in a logic, and so on. The physicalmanipulations create a concrete, tangible, useful, real-world result.

It has proven convenient at times, principally for reasons of commonusage, to refer to these signals as bits, values, elements, symbols,characters, terms, numbers, and so on. It should be borne in mind,however, that these and similar terms are to be associated with theappropriate physical quantities and are merely convenient labels appliedto these quantities. Unless specifically stated otherwise, it isappreciated that throughout the description, terms including processing,computing, determining, and so on, refer to actions and processes of acomputer system, logic, processor, or similar electronic device thatmanipulates and transforms data represented as physical (electronic)quantities.

Example methods may be better appreciated with reference to flowdiagrams. While for purposes of simplicity of explanation, theillustrated methodologies are shown and described as a series of blocks,it is to be appreciated that the methodologies are not limited by theorder of the blocks, as some blocks can occur in different orders and/orconcurrently with other blocks from that shown and described. Moreover,less than all the illustrated blocks may be required to implement anexample methodology. Blocks may be combined or separated into multiplecomponents. Furthermore, additional and/or alternative methodologies canemploy additional, not illustrated blocks.

FIG. 1 illustrates a method 100 that facilitates selecting aphase-encoding direction for an MRI session. In one example, method 100will determine an optimal phase-encoding direction. In another example,method 100 will determine a superior (e.g., best-available)phase-encoding direction of those phase-encoding directions tested. Inanother example, method 100 will predict a superior phase-encodingdirection based on the phase-encoding directions tested. Based on timeand/or processing constraints, a “superior” solution may be determinedinstead of an “optimal” solution.

Method 100 includes, at 110, selecting a set of projection directionsalong which an MRI apparatus is to acquire projections of an object tobe imaged. The object may be, for example, a portion of a human. The setof projection directions may include two or more directions (e.g., 2°,4°, 45°, 90°, 360°). The set of projection directions may be selectedbased, for example, on a user input, on a default configuration, on aconfiguration associated with a body part to be imaged, on aconfiguration associated with a type of MRI session to be performed, andso on. Different sized sets may be employed in different examples.

Method 100 also includes, at 120, controlling the MRI apparatus toproduce gradients that produce the set of projection directions.Controlling the MRI apparatus may include, for example, providing asignal to the MRI apparatus, writing a value to a memory in the MRIapparatus, activating a circuit in the MRI apparatus, uploading a fileto a control computer associated with the MRI apparatus, and so on.

Method 100 also includes, at 130, controlling the MRI apparatus toacquire MR signal from the object through a set of detectors. In oneexample, the set of detectors may be a phased array of coils.Controlling the MRI apparatus to acquire the MR signal may include, forexample, providing a signal to the MRI apparatus, writing a value to aregister in a control circuit associated with the MRI apparatus,providing a voltage to a control circuit associated with the MRIapparatus, and so on.

Method 100 also includes, at 140, analyzing the MR signal to identifyindividual sensitivities for members of the set of detectors. Analyzingthe MR signal may include performing a signal decomposition on the MRsignal. The signal decomposition may be, for example, a principalcomponent analysis (PCA), a singular value decomposition (SVD), aspectral decomposition, a polar decomposition, a quadratic residue (QR)decomposition, and so on. Analyzing the MR signal may include producinga spatial harmonic sensitivity profile for the phased array of coils. Inone example, the spatial harmonic sensitivity profile may be based onequation [1].

Method 100 also includes, at 150, selecting a phase-encoding directionfor a pMRI session based, at least in part, on the individualsensitivities of the members. In one example, the phase-encodingdirection may be associated with the projection direction that yieldedthe largest signal. In another example, the phase-encoding direction maynot be associated with the projection direction that yielded the largestsignal, but rather may be associated with a projection direction(s) thatyielded an acceptable signal. For example, in some applications (e.g.,arterial spin labeling (ASL)), possible projection directions and thuspossible phase-encoding directions may be limited due to relationshipsbetween anatomical positioning, patient positioning, blood flowdirection(s), gating issues, and so on. Thus, a “best-available”phase-encoding direction may be selected even though it produces a lowersignal than an “optimal” phase-encoding direction.

Method 100 also includes, at 160, controlling the MRI apparatus toperform the pMRI session based, at least in part, on the selectedphase-encoding direction. Once again controlling the MRI apparatus mayinclude writing a value to the MRI apparatus, providing a signal to theMRI apparatus, providing a voltage to a circuit in the MRI apparatus,invoking a stored procedure on the MRI apparatus, and so on.

While FIG. 1 illustrates various actions occurring in serial, it is tobe appreciated that various actions illustrated in FIG. 1 could occursubstantially in parallel. By way of illustration, a first process couldselect projection directions and control an MRI apparatus to producegradients that produce a set of projection directions, a second processcould control the MRI apparatus to acquire signal, and a third processcould analyze received signal and select a phase-encoding direction fora pMRI session. While three processes are described, it is to beappreciated that a greater and/or lesser number of processes could beemployed and that lightweight processes, regular processes, threads, andother approaches could be employed.

In one example, a method may be implemented as computer executableinstructions. Thus, in one example, a computer-readable medium may storecomputer executable instructions that if executed by a machine (e.g.,processor) cause the machine to perform method 100. While executableinstructions associated with the method 100 are described as beingstored on a computer-readable medium, it is to be appreciated thatexecutable instructions associated with other example methods describedherein may also be stored on a computer-readable medium.

FIG. 5 illustrates a method 500 associated with determining aphase-encoding direction(s) for a pMRI session(s). Method 500 includes,at 510, acquiring a set of data from a phased array of coils in a pMRIapparatus. The set of data may be associated with projections along afirst set of spatial directions. To be able to compare differentencoding capabilities, the first set of spatial directions is to includeat least two projection directions. While at least two projectiondirections are described, it is to be appreciated that a greater numberof projection directions may be employed. For example, an evenly spacedset of 15 projections may divide a 360 degree problem space into 24equal slices. In another example, an unevenly spaced set of projectionsmay divide a problem space into unequal slices.

Method 500 may also include, at 520, determining an encoding capabilityfor the spatial directions based on a signal decomposition of the set ofdata. The signal decomposition may be performed using variousdecomposition techniques described herein (e.g., PCA, SVD).

Method 500 may include, at 530, determining whether data acquisition andencoding capability have been completed. The determination may depend,for example, on the nature of the different encoding capabilitiesdetermined at 520 from the data acquired at 510. For example, if thedifferent encoding capabilities differ by a threshold amount and/orprovide a signal intensity that exceeds a threshold amount, then thedetermination at 530 may be yes. If, however, the different encodingcapabilities determined at 520 do not exceed a difference threshold ordo not provide a minimum acceptable signal intensity, then thedetermination at 530 may be no.

Method 500 includes, at 540, selecting a phase-encoding direction basedon the encoding capabilities determined at 520. In one example, thephase-encoding direction may be associated with a highest determinedencoding capability. In another example, the phase-encoding directionmay be associated with a determined encoding capability that exceeds anencoding capability threshold. This phase-encoding direction may not bethe highest determined encoding possible, but may be acceptable and maybe determined within an acceptable time frame.

Method 500 also includes, at 550, providing a signal corresponding tothe selected phase-encoding direction. The signal is provided after thephase-encoding direction is selected after at least two applications ofgradients to produce at least two different projection directions. Theimaging volume may be, for example, a portion of an item (e.g., humanbody) in an MRI apparatus. A phased array detector in an MRI apparatusreceives signals in response to the applications of the RF energy anddata associated with the signals is then analyzed using a signaldecomposition (e.g., PCA, SVD, QR). Based on the signal decompositionanalysis, an optimal and/or desired phase-encoding direction may beselected. To determine the encoding capability along a single spatialdirection, a signal decomposition is performed for a single projectionalong that direction. In one example, this projection may be measureddirectly. In another example, this projection may be obtained after aRadon transform of a pre-scan of the imaging volume.

FIG. 6 illustrates a method 600 associated with determiningphase-encoding direction for pMRI where the method includes multipleiterations of test signal acquisition. Consider an MRI apparatus thatcan produce projections along 360 directions (e.g., one per degree in acircle). While 360 projections may be possible, it may be tootime-consuming to acquire and analyze all 360 projections. Thus, in oneexample, a first set of encoding directions may be examined. Forexample, four encoding directions that differ from each other by fifteendegrees may be taken. The four data sets may then be analyzed. Adetermination may then be made about candidate projection directions.For example, the four sets of data may indicate that pMRI performancewill improve when the projection direction moves away from a firstprojection direction and pMRI performance may decrease when theprojection direction moves towards the first projection direction. Thus,candidate direction(s) may be selected for subsequent analysis based onthe initial set of projection directions. The candidate directions maythen be analyzed to select an optimal or superior phase-encodingdirection. One skilled in the art will appreciate that variouspartitioning techniques (e.g., binary search) may be performed to reduceand/or minimize the number of projections employed to identify anoptimal, improved, and/or adequate projection direction.

After a desired phase-encoding direction is selected, a signalcorresponding to the direction selected is provided. This signal may beprovided, for example, to a pMRI control logic to facilitate controllinga pMRI apparatus to perform a pMRI session based on the desiredphase-encoding direction. In one example, the phase-encoding directionmay be an optimal direction. In another example, the phase-encodingdirection may not be the optimal direction, but may be a best directiondetected and/or predicted in an allotted amount of time using anallotted amount of resources to perform an allotted number ofprojections. This signal may then be used to control an MRI apparatusduring a pMRI session.

Thus, method 600 includes, at 610, acquiring a first set of data from aphased array of coils in a pMRI apparatus. The first set of data may beassociated with projections along a first set of spatial directions.Method 600 may also include, at 620, determining a first encodingcapability for the first spatial directions. The encoding capabilitiesmay be based on a signal decomposition (e.g., PCA) of the first set ofdata. Having determined the capabilities at 620, method 600 may proceed,at 630, to select a first phase-encoding direction based on thedetermined first encoding capability. This first phase-encodingdirection may be determined by comparing the sensitivities determinedfor the different projections and predicting a direction that willproduce a desired result (e.g., varied spatial encoding values).

Method 600 may then proceed, at 640, to acquire a second set of datafrom the phased array of coils. The second set of data will beassociated with projections along a second set of spatial directions.The second set of spatial directions will be selected based, at least inpart, on the first phase-encoding direction. For example, the second setof spatial directions may bracket and fan out from the firstphase-encoding direction. In this way, coil sensitivities associatedwith relevant projection directions may be examined and coilsensitivities associated with irrelevant projections may be ignored.

Method 600 may then proceed, at 650, by determining encodingcapabilities for the second spatial directions. Once again the encodingcapabilities may be based on signal decomposition (e.g., PCA) of thesecond set of data. At 660 a determination may be made concerningwhether to iterate through acquiring another data set(s) based on theencoding capabilities determined at 650. The determination may depend,for example, on whether the encoding capabilities satisfy a differencethreshold, on whether the encoding capabilities satisfy an intensitythreshold, on whether additional time is available, and so on.

If the determination at 660 is no, then processing returns to 640 whereanother set of data is acquired based on projections related to encodingcapabilities and directions analyzed at 650. One skilled in the art willappreciate that method 600 may iterate though actions 640, 650, and 660multiple times. If the determination at 660 is yes, then processingmoves on to 670 where a second phase-encoding direction is selected. Thesecond phase-encoding direction may be selected based, at least in part,on the determined second encoding capability. In different examples thesecond phase-encoding direction may be an optimal direction, may be thefirst direction that satisfies a threshold, and so on.

Method 600 concludes, at 680, by providing a signal corresponding to thesecond phase-encoding direction. The signal may be provided to a pMRIcontrol logic that controls phase encoding for a pMRI session.

FIG. 7 illustrates a pMRI apparatus 700. Apparatus 700 includes a phasedarray of signal receiving coils 710. The phased array 710 is to receiveMR signals from an object to which the pMRI apparatus 700 applies RFenergy. The pMRI apparatus 700 is to produce gradients that produce aset of projection directions.

Apparatus 700 includes an image acquisition logic 720 to reconstruct animage from the MR signals. Image quality may depend on the spatialencoding capabilities of the phased array 710. Therefore apparatus 700includes a phase-encoding direction selection logic 730. Logic 730 is toperform a signal decomposition on test data acquired from the phasedarray 710. The test data is acquired in response to the pMRI apparatus700 producing gradients that produce different projections in differentdirections during a calibration phase. The signal decompositionperformed by logic 730 is to measure the encoding capability of thephased array 710 for the different directions. The encoding capabilitiesare then examined to facilitate selecting a phase-encoding direction fora pMRI session. The phase-encoding direction is to be based, at least inpart, on the encoding capability of the phased array 710 as determinedby the logic 730. In one example, the image acquisition logic 720 iscontrolled to reconstruct at least a portion of an image based on aphase-encoding direction as selected by the phase-encoding directionselection logic 730

FIG. 8 illustrates an example MRI apparatus 800 configured with a phaseencoding direction control apparatus 899 to facilitate intelligentlyselecting a phase encoding direction for a pMRI session based onfeedback received in response to gradients being applied to producedifferent projections. The phase encoding direction control apparatus899 may be configured with elements of example apparatus describedherein and/or may perform example methods described herein.

The apparatus 800 includes a basic field magnet(s) 810 and a basic fieldmagnet supply 820. Ideally, the basic field magnets 810 would produce auniform Bo field. However, in practice, the Bo field may not be uniform,and may vary over an object being imaged by the MRI apparatus 800. MRIapparatus 800 may include gradient coils 830 configured to emit gradientmagnetic fields like G_(S), G_(P) and G_(R). The gradient coils 830 maybe controlled, at least in part, by a gradient coils supply 840. In someexamples, the timing, strength, and orientation of the gradient magneticfields may be controlled, and thus selectively adapted during an MRIprocedure.

MRI apparatus 800 may include a set of RF antennas 850 that areconfigured to generate RF pulses and to receive resulting magneticresonance signals from an object to which the RF pulses are directed. Insome examples, how the pulses are generated and how the resulting MRsignals are received may be controlled and thus may be selectivelyadapted during an MRI procedure. Separate RF transmission and receptioncoils can be employed. The RF antennas 850 may be controlled, at leastin part, by a set of RF transmission units 860. An RF transmission unit860 may provide a signal to an RF antenna 850.

The gradient coils supply 840 and the RF transmission units 860 may becontrolled, at least in part, by a control computer 870. In one example,the control computer 870 may be programmed to control a pMRI device asdescribed herein. The magnetic resonance signals received from the RFantennas 850 can be employed to generate an image and thus may besubject to a transformation process like a two dimensional FFT thatgenerates pixilated image data. The transformation can be performed byan image computer 880 or other similar processing device. The image datamay then be shown on a display 890. While FIG. 8 illustrates an exampleMRI apparatus 800 that includes various components connected in variousways, it is to be appreciated that other MRI apparatus may include othercomponents connected in other ways.

FIG. 9 illustrates an example computing device in which example methodsdescribed herein, and equivalents, may operate. The example computingdevice may be a computer 900 that includes a processor 902, a memory904, and input/output ports 910 operably connected by a bus 908. In oneexample, the computer 900 may include a phase-encoding selection andcontrol logic 930 to facilitate controlling a pMRI apparatus withrespect to phase-encoding direction for a pMRI session. In differentexamples, the logic 930 may be implemented in hardware, software,firmware, and/or combinations thereof. While the logic 930 isillustrated as a hardware component attached to the bus 908, it is to beappreciated that in one example, the logic 930 could be implemented inthe processor 902.

Thus, logic 930 may provide means (e.g., hardware, software, firmware)for acquiring a set of test signals from a phased array of coilsassociated with a pMRI apparatus. In one example, the set of testsignals are produced in response to gradients being applied to produce aset of different projection directions. The means may be implemented,for example, as an ASIC programmed to control transmit and/or receiveantennas. The means may also be implemented as computer executableinstructions that are presented to computer 900 as data 916 that aretemporarily stored in memory 904 and then executed by processor 902.Logic 930 may also provide means (e.g., hardware, software, firmware)for determining individual coil sensitivities for members of the phasedarray of coils for the different projection directions. The individualcoil sensitivities may be determined, for example, in response to signaldecomposition. Logic 930 may also provide means (e.g., hardware,software, firmware) for controlling a pMRI apparatus to perform a pMRIsession where gradients are applied to produce a selected projectiondirection. The selected projection direction is based, at least in part,on the determined individual coil sensitivities.

Generally describing an example configuration of the computer 900, theprocessor 902 may be a variety of various processors including dualmicroprocessor and other multi-processor architectures. A memory 904 mayinclude volatile memory and/or non-volatile memory. Non-volatile memorymay include, for example, ROM, PROM, and so on. Volatile memory mayinclude, for example, RAM, SRAM, DRAM, and so on.

A disk 906 may be operably connected to the computer 900 via, forexample, an input/output interface (e.g., card, device) 918 and aninput/output port 910. The disk 906 may be, for example, a magnetic diskdrive, a solid state disk drive, a floppy disk drive, a tape drive, aZip drive, a flash memory card, a memory stick, and so on. Furthermore,the disk 906 may be a CD-ROM drive, a CD-R drive, a CD-RW drive, a DVDROM, and so on. The memory 904 can store a process 914 and/or a data916, for example. The disk 906 and/or the memory 904 can store anoperating system that controls and allocates resources of the computer900.

The bus 908 may be a single internal bus interconnect architectureand/or other bus or mesh architectures. While a single bus isillustrated, it is to be appreciated that the computer 900 maycommunicate with various devices, logics, and peripherals using otherbusses (e.g., PCIE, 1394, USB, Ethernet). The bus 908 can be typesincluding, for example, a memory bus, a memory controller, a peripheralbus, an external bus, a crossbar switch, and/or a local bus.

The computer 900 may interact with input/output devices via the i/ointerfaces 918 and the input/output ports 910. Input/output devices maybe, for example, a keyboard, a microphone, a pointing and selectiondevice, cameras, video cards, displays, the disk 906, the networkdevices 920, and so on. The input/output ports 910 may include, forexample, serial ports, parallel ports, and USB ports. The computer 900can operate in a network environment and thus may be connected to thenetwork devices 920 via the i/o interfaces 918, and/or the i/o ports910. Through the network devices 920, the computer 900 may interact witha network. Through the network, the computer 900 may be logicallyconnected to remote computers. Networks with which the computer 900 mayinteract include, but are not limited to, a LAN, a WAN, and othernetworks.

In one example, computer 900 may be programmed to select aphase-encoding direction for an MRI session in a pMRI apparatus based onthe computed encoding capability of a phased array of receiver coils inthe pMRI apparatus. The encoding capability may be computed by a PCA ofdata sets associated with sets of signals received in the phased arrayin response to gradients that produce a set of projection directions andRF energy being applied to an item in the MRI apparatus. The computer900 may then provide a signal corresponding to the selectedphase-encoding direction.

While example systems, methods, and so on have been illustrated bydescribing examples, and while the examples have been described inconsiderable detail, it is not the intention of the applicants torestrict or in any way limit the scope of the appended claims to suchdetail. It is, of course, not possible to describe every conceivablecombination of components or methodologies for purposes of describingthe systems, methods, and so on described herein. Therefore, theinvention is not limited to the specific details, the representativeapparatus, and illustrative examples shown and described. Thus, thisapplication is intended to embrace alterations, modifications, andvariations that fall within the scope of the appended claims.

To the extent that the term “includes” or “including” is employed in thedetailed description or the claims, it is intended to be inclusive in amanner similar to the term “comprising” as that term is interpreted whenemployed as a transitional word in a claim.

To the extent that the term “or” is employed in the detailed descriptionor claims (e.g., A or B) it is intended to mean “A or B or both”. Whenthe applicants intend to indicate “only A or B but not both” then theterm “only A or B but not both” will be employed. Thus, use of the term“or” herein is the inclusive, and not the exclusive use. See, Bryan A.Gamer, A Dictionary of Modern Legal Usage 624 (2d. Ed. 1995).

To the extent that the phrase “one or more of, A, B, and C” is employedherein, (e.g., a data store configured to store one or more of, A, B,and C) it is intended to convey the set of possibilities A, B, C, AB,AC, BC, and/or ABC (e.g., the data store may store only A, only B, onlyC, A&B, A&C, B&C, and/or A&B&C). It is not intended to require one of A,one of B, and one of C. When the applicants intend to indicate “at leastone of A, at least one of B, and at least one of C”, then the phrasing“at least one of A, at least one of B, and at least one of C” will beemployed.

1. A computer-readable medium storing computer executable instructionsthat when executed by a computer cause the computer to perform a method,the method comprising: selecting a set of projection directions alongwhich a magnetic resonance imaging (MRI) apparatus is to acquireprojections of an object to be imaged; controlling the MRI apparatus toapply magnetic field gradients to generate projections along the set ofprojection directions; controlling the MRI apparatus to an acquire MRsignal from the object through a set of detectors; analyzing the MRsignal to identify individual sensitivities for members of the set ofdetectors; selecting a phase-encoding direction for a parallel MRI(pMRI) session based, at least in part, on the individual sensitivitiesfor the members; and controlling the MRI apparatus to perform the pMRIsession based, at least in part, on the selected phase-encodingdirection.
 2. The computer-readable medium of claim 1, where the set ofprojection directions includes at least two different projectiondirections.
 3. The computer-readable medium of claim 2, the set ofdetectors comprising a phased array of coils.
 4. The computer-readablemedium of claim 3, where analyzing the MR signal includes performing asignal decomposition on the MR signal.
 5. The computer-readable mediumof claim 4, the signal decomposition being one of, a principal componentanalysis (PCA), a singular value decomposition (SVD), a spectraldecomposition, a polar decomposition, and a quadratic residue (QR)decomposition.
 6. The computer-readable medium of claim 4, whereanalyzing the MR signal includes producing a spatial harmonicsensitivity profile for the phased array of coils.
 7. Thecomputer-readable medium of claim 6, where the spatial harmonicsensitivity profile is based, at least in part, on:${\sum\limits_{e}{n_{e}^{1}{C_{e}(x)}}} = ^{{{}_{}^{\; \Delta}{}_{}^{}}X}$where: n represents a linear weight, and C represents a coil sensitivityprofile.
 8. A method, comprising: acquiring a set of data from a phasedarray of coils in a parallel MRI (pMRI) apparatus, the set of data beingassociated with projections along a set of spatial directions;determining an encoding capability for the set of spatial directionsbased on a signal decomposition of the set of data; selecting aphase-encoding direction based on the determined encoding capability;and providing a signal corresponding to the selected phase-encodingdirection.
 9. The method of claim 8, the signal decomposition being oneof, a principal component analysis (PCA), a singular value decomposition(SVD), a spectral decomposition, a polar decomposition, and a quadraticresidue (QR) decomposition.
 10. The method of claim 8, where thephase-encoding direction is associated with a highest determinedencoding capability.
 11. The method of claim 8, where the phase-encodingdirection is associated with a determined encoding capability thatexceeds an encoding capability threshold.
 12. A method, comprising:acquiring a first set of data from a phased array of coils in a parallelMRI (pMRI) apparatus, the first set of data being associated withprojections along a first set of first spatial directions; determining afirst set of encoding capabilities for the first set of spatialdirections based on a signal decomposition of the first set of data;selecting a first phase-encoding direction based on the first set ofencoding capabilities; acquiring a second set of data from the phasedarray of coils, the second set of data being associated with projectionsalong a second set of second spatial directions, the second set ofspatial directions being selected based, at least in part, on the firstphase-encoding direction; determining a second set of encodingcapabilities for the second set of spatial directions based on thesignal decomposition of the second set of data; selecting a secondphase-encoding direction based on the second set of encodingcapabilities; and providing a signal corresponding to the secondphase-encoding direction.
 13. The method of claim 12, the method beingperformed in a pMRI apparatus.
 14. A pMRI apparatus, comprising: aphased array of signal receiving coils to receive MR signals; an imageacquisition logic to reconstruct an image from the MR signals; and aphase-encoding direction selection logic to perform a signaldecomposition on data acquired from the phased array of signal coils inresponse to the pMRI apparatus acquiring different projections indifferent directions, where the signal decomposition is to measure theencoding capability of the phased array for the different directions,and to select a phase-encoding direction for a pMRI session based, atleast in part, on the encoding capability of the phased array asdetermined by the signal decomposition.
 15. The pMRI apparatus of claim14, the image acquisition logic being controlled to reconstruct at leasta portion of an image based on a phase-encoding direction as selected bythe phase-encoding direction selection logic.
 16. The pMRI apparatus ofclaim 14, where the signal decomposition is one of a principal componentanalysis (PCA), a singular value decomposition (SVD), a spectraldecomposition, a polar decomposition, and a quadratic residue (QR)decomposition.
 17. The pMRI apparatus of claim 14, where thephase-encoding direction selection logic is to produce a spatialharmonic sensitivity profile of the phased array of signal receivingcoils.
 18. The pMRI apparatus of claim 17, where the spatial harmonicsensitivity profile is based, at least in part, on:${\sum\limits_{e}{n_{e}^{1}{C_{e}(x)}}} = ^{{{}_{}^{\; \Delta}{}_{}^{}}X}$where: n represents a linear weight, and C represents a coil sensitivityprofile.
 19. A system, comprising: means for acquiring a set of testsignals from a phased array of coils associated with a parallel magneticresonance imaging (pMRI) apparatus, where the set of test signals areproduced in response to the application of field gradients that producea set of different projection directions; means for determiningindividual coil sensitivities for members of the phased array of coilsfor the different projection directions; and means for controlling thepMRI apparatus to perform a pMRI session where the object is imagedalong a selected projection direction, based, at least in part, on thedetermined individual coil sensitivities.
 20. A computer programmed toselect a phase-encoding direction for a magnetic resonance imaging (MRI)session in a parallel MRI (pMRI) apparatus based on the computedencoding capability of a phased array of receiver coils in the pMRIapparatus, where the encoding capability is computed by a principalcomponent analysis (PCA) of two or more data sets associated with two ormore sets of signals received in the phased array in response to radiofrequency (RF) energy applied to an item in the MRI apparatus encodedalong two or more spatial directions, and to provide a signalcorresponding to the selected phase-encoding direction.